---
title: AzureChatOpenAI
---

Azure OpenAI is a Microsoft Azure service that provides powerful language models from OpenAI.

This will help you getting started with AzureChatOpenAI [chat models](/oss/langchain/models). For detailed documentation of all AzureChatOpenAI features and configurations head to the [API reference](https://api.js.langchain.com/classes/langchain_openai.AzureChatOpenAI.html).

## Overview

### Integration details

| Class | Package | Local | Serializable | [PY support](https://python.langchain.com/docs/integrations/chat/azure_chat_openai) | Downloads | Version |
| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |
| [AzureChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.AzureChatOpenAI.html) | [`@langchain/openai`](https://www.npmjs.com/package/@langchain/openai) | ❌ | ✅ | ✅ | ![NPM - Downloads](https://img.shields.io/npm/dm/@langchain/openai?style=flat-square&label=%20&) | ![NPM - Version](https://img.shields.io/npm/v/@langchain/openai?style=flat-square&label=%20&) |

### Model features

See the links in the table headers below for guides on how to use specific features.

| [Tool calling](/oss/langchain/tools) | [Structured output](/oss/langchain/structured-output) | JSON mode | [Image input](/oss/how-to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/oss/langchain/streaming/) | [Token usage](/oss/how-to/chat_token_usage_tracking/) | [Logprobs](/oss/how-to/logprobs/) |
| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: |
| ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ |

## Setup

[Azure OpenAI](https://azure.microsoft.com/products/ai-services/openai-service/) is a cloud service to help you quickly develop generative AI experiences with a diverse set of prebuilt and curated models from OpenAI, Meta and beyond.

LangChain.js supports integration with [Azure OpenAI](https://azure.microsoft.com/products/ai-services/openai-service/) using the new Azure integration in the [OpenAI SDK](https://github.com/openai/openai-node).

You can learn more about Azure OpenAI and its difference with the OpenAI API on [this page](https://learn.microsoft.com/azure/ai-services/openai/overview).

### Credentials

If you don't have an Azure account, you can [create a free account](https://azure.microsoft.com/free/) to get started.

You'll also need to have an Azure OpenAI instance deployed. You can deploy a version on Azure Portal following [this guide](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource?pivots=web-portal).

Once you have your instance running, make sure you have the name of your instance and key. You can find the key in the Azure Portal, under the "Keys and Endpoint" section of your instance. Then, if using Node.js, you can set your credentials as environment variables:

```bash
AZURE_OPENAI_API_INSTANCE_NAME=<YOUR_INSTANCE_NAME>
AZURE_OPENAI_API_DEPLOYMENT_NAME=<YOUR_DEPLOYMENT_NAME>
AZURE_OPENAI_API_KEY=<YOUR_KEY>
AZURE_OPENAI_API_VERSION="2024-02-01"
```

If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:

```bash
# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"
```

### Installation

The LangChain AzureChatOpenAI integration lives in the `@langchain/openai` package:

```{=mdx}

import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>

<Npm2Yarn>
  @langchain/openai @langchain/core
</Npm2Yarn>

```

## Instantiation

Now we can instantiate our model object and generate chat completions:

```typescript
import { AzureChatOpenAI } from "@langchain/openai"

const llm = new AzureChatOpenAI({
    model: "gpt-4o",
    temperature: 0,
    maxTokens: undefined,
    maxRetries: 2,
    azureOpenAIApiKey: process.env.AZURE_OPENAI_API_KEY, // In Node.js defaults to process.env.AZURE_OPENAI_API_KEY
    azureOpenAIApiInstanceName: process.env.AZURE_OPENAI_API_INSTANCE_NAME, // In Node.js defaults to process.env.AZURE_OPENAI_API_INSTANCE_NAME
    azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME, // In Node.js defaults to process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME
    azureOpenAIApiVersion: process.env.AZURE_OPENAI_API_VERSION, // In Node.js defaults to process.env.AZURE_OPENAI_API_VERSION
})
```

## Invocation

```typescript
const aiMsg = await llm.invoke([
    [
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ],
    ["human", "I love programming."],
])
aiMsg
```

```output
AIMessage {
  "id": "chatcmpl-9qrWKByvVrzWMxSn8joRZAklHoB32",
  "content": "J'adore la programmation.",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "completionTokens": 8,
      "promptTokens": 31,
      "totalTokens": 39
    },
    "finish_reason": "stop"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 31,
    "output_tokens": 8,
    "total_tokens": 39
  }
}
```

```typescript
console.log(aiMsg.content)
```

```output
J'adore la programmation.
```

## Chaining

We can [chain](/oss/how-to/sequence/) our model with a prompt template like so:

```typescript
import { ChatPromptTemplate } from "@langchain/core/prompts"

const prompt = ChatPromptTemplate.fromMessages(
    [
        [
            "system",
            "You are a helpful assistant that translates {input_language} to {output_language}.",
        ],
        ["human", "{input}"],
    ]
)

const chain = prompt.pipe(llm);
await chain.invoke(
    {
        input_language: "English",
        output_language: "German",
        input: "I love programming.",
    }
)
```

```output
AIMessage {
  "id": "chatcmpl-9qrWR7WiNjZ3leSG4Wd77cnKEVivv",
  "content": "Ich liebe das Programmieren.",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "completionTokens": 6,
      "promptTokens": 26,
      "totalTokens": 32
    },
    "finish_reason": "stop"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 26,
    "output_tokens": 6,
    "total_tokens": 32
  }
}
```

## Using Azure Managed Identity

If you're using Azure Managed Identity, you can configure the credentials like this:

```typescript
import {
  DefaultAzureCredential,
  getBearerTokenProvider,
} from "@azure/identity";
import { AzureChatOpenAI } from "@langchain/openai";

const credentials = new DefaultAzureCredential();
const azureADTokenProvider = getBearerTokenProvider(
  credentials,
  "https://cognitiveservices.azure.com/.default"
);

const llmWithManagedIdentity = new AzureChatOpenAI({
  azureADTokenProvider,
  azureOpenAIApiInstanceName: "<your_instance_name>",
  azureOpenAIApiDeploymentName: "<your_deployment_name>",
  azureOpenAIApiVersion: "<api_version>",
});
```

## Using a different domain

If your instance is hosted under a domain other than the default `openai.azure.com`, you'll need to use the alternate `AZURE_OPENAI_BASE_PATH` environment variable.
For example, here's how you would connect to the domain `https://westeurope.api.microsoft.com/openai/deployments/{DEPLOYMENT_NAME}`:

```typescript
import { AzureChatOpenAI } from "@langchain/openai";

const llmWithDifferentDomain = new AzureChatOpenAI({
  temperature: 0.9,
  azureOpenAIApiKey: "<your_key>", // In Node.js defaults to process.env.AZURE_OPENAI_API_KEY
  azureOpenAIApiDeploymentName: "<your_deployment_name>", // In Node.js defaults to process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME
  azureOpenAIApiVersion: "<api_version>", // In Node.js defaults to process.env.AZURE_OPENAI_API_VERSION
  azureOpenAIBasePath:
    "https://westeurope.api.microsoft.com/openai/deployments", // In Node.js defaults to process.env.AZURE_OPENAI_BASE_PATH
});

```

## Custom headers

You can specify custom headers by passing in a `configuration` field:

```typescript
import { AzureChatOpenAI } from "@langchain/openai";

const llmWithCustomHeaders = new AzureChatOpenAI({
  azureOpenAIApiKey: process.env.AZURE_OPENAI_API_KEY, // In Node.js defaults to process.env.AZURE_OPENAI_API_KEY
  azureOpenAIApiInstanceName: process.env.AZURE_OPENAI_API_INSTANCE_NAME, // In Node.js defaults to process.env.AZURE_OPENAI_API_INSTANCE_NAME
  azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME, // In Node.js defaults to process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME
  azureOpenAIApiVersion: process.env.AZURE_OPENAI_API_VERSION, // In Node.js defaults to process.env.AZURE_OPENAI_API_VERSION
  configuration: {
    defaultHeaders: {
      "x-custom-header": `SOME_VALUE`,
    },
  },
});

await llmWithCustomHeaders.invoke("Hi there!");
```

The `configuration` field also accepts other `ClientOptions` parameters accepted by the official SDK.

**Note:** The specific header `api-key` currently cannot be overridden in this manner and will pass through the value from `azureOpenAIApiKey`.

## Migration from Azure OpenAI SDK

If you are using the deprecated Azure OpenAI SDK with the `@langchain/azure-openai` package, you can update your code to use the new Azure integration following these steps:

1. Install the new `@langchain/openai` package and remove the previous `@langchain/azure-openai` package:

```{=mdx}

<Npm2Yarn>
  @langchain/openai
</Npm2Yarn>

```

```bash
npm uninstall @langchain/azure-openai
```

2. Update your imports to use the new `AzureChatOpenAI` class from the `@langchain/openai` package:

   ```typescript
   import { AzureChatOpenAI } from "@langchain/openai";
   ```

3. Update your code to use the new `AzureChatOpenAI` class and pass the required parameters:

   ```typescript
   const model = new AzureChatOpenAI({
     azureOpenAIApiKey: "<your_key>",
     azureOpenAIApiInstanceName: "<your_instance_name>",
     azureOpenAIApiDeploymentName: "<your_deployment_name>",
     azureOpenAIApiVersion: "<api_version>",
   });
   ```

   Notice that the constructor now requires the `azureOpenAIApiInstanceName` parameter instead of the `azureOpenAIEndpoint` parameter, and adds the `azureOpenAIApiVersion` parameter to specify the API version.

   - If you were using Azure Managed Identity, you now need to use the `azureADTokenProvider` parameter to the constructor instead of `credentials`, see the [Azure Managed Identity](#using-azure-managed-identity) section for more details.

   - If you were using environment variables, you now have to set the `AZURE_OPENAI_API_INSTANCE_NAME` environment variable instead of `AZURE_OPENAI_API_ENDPOINT`, and add the `AZURE_OPENAI_API_VERSION` environment variable to specify the API version.

## API reference

For detailed documentation of all AzureChatOpenAI features and configurations head to the [API reference](https://api.js.langchain.com/classes/langchain_openai.AzureChatOpenAI.html).
